Substrate Image Color Analysis for CMP Film Non-Uniformity
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Solution Overview
Problem
Existing optical metrology systems for CMP processes are inefficient in detecting and classifying film non-uniformity and abnormalities on substrates, leading to challenges in real-time process control and throughput.
Innovation Solution
A method utilizing color images of substrates to classify film non-uniformity by generating difference vectors between pixel colors and a standard color, sorting pixels into normal and abnormal regions, and using machine learning to determine the type and severity of abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optical metrology systems are used to measure film thickness, then measurement capability is provided, but detection speed and classification efficiency are insufficient for real-time process control
Solution Approach 1:
The patent replaces traditional mechanical/optical metrology measurement systems with a color-based image processing system. Instead of using complex optical instruments to measure film thickness, the system captures color images of the substrate and analyzes color variations to detect and classify film abnormalities. This substitution dramatically increases detection speed while maintaining sufficient measurement capability for process control.
Solution Approach 2:
The patent exploits color changes in the substrate image to detect film non-uniformity. By capturing color images and analyzing variations in color values across different regions, the system can identify abnormal areas indicating film defects. The method converts optical measurement information into color signal analysis, enabling fast parallel processing of entire substrate surfaces.
2Measurement precision
If comprehensive film analysis is performed to improve detection accuracy, then classification precision improves, but processing time increases
Solution Approach 1:
The patent segments the substrate surface into multiple regions based on color analysis, allowing parallel processing of different areas. By dividing the large-scale substrate image into manageable segments, the system can perform comprehensive analysis on each segment independently and simultaneously, reducing overall processing time while maintaining high detection accuracy for each region.
Solution Approach 2:
The patent applies partial action by focusing analysis on detecting and classifying abnormal regions rather than performing exhaustive measurements across the entire substrate. The system identifies regions with color deviations from the standard and concentrates processing resources on these areas, achieving high detection accuracy for abnormalities while minimizing processing time for normal regions.
Data Source
AI summary
A method of classification of a film non-uniformity on a substrate includes obtaining a color image of a substrate with the color image comprising a plurality of color channels, obtaining a standard color for the color image of the substrate, for each respective pixel along a path in the color image determining a difference vector between the a color of the respective pixel and the standard color to generate a sequence of difference vectors, and sorting the pixels along the path into a plurality of regions including at least one normal region and at least one abnormal region based on the sequence of difference vectors, including comparing a multiplicity of the difference vectors in the sequence to a threshold.